DAOS as HPC Storage, a view from Numerical Weather Prediction

Nicolau Manubens Gil, Tiago Quintino, Simon D. Smart, Emanuele Danovaro, William Adrian Jackson

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

Object storage solutions potentially address long-standing performance issues with POSIX file systems for certain I/O workloads, and new storage technologies offer promising performance characteristics for data-intensive use cases.

In this work, we present a preliminary assessment of Intel's Distributed Asynchronous Object Store (DAOS), an emerging high-performance object store, in conjunction with non-volatile storage and evaluate its potential use for HPC storage. We demonstrate DAOS can provide the required performance, with bandwidth scaling linearly with additional DAOS server nodes in most cases, although choices in configuration and application design can impact achievable bandwidth. We describe a new I/O benchmark and associated metrics that address object storage performance from application-derived workloads.
Original languageEnglish
Number of pages12
Publication statusPublished - 2023
Event37th IEEE International Parallel and Distributed Processing Symposium - St Petersburg, United States
Duration: 15 May 202319 May 2023
Conference number: 37


Conference37th IEEE International Parallel and Distributed Processing Symposium
Abbreviated titleIPDPS
Country/TerritoryUnited States
CitySt Petersburg

Keywords / Materials (for Non-textual outputs)

  • scalable object storage
  • next-generation I/O
  • Storage class memory
  • non-volatile memory
  • Numerical weather prediction
  • DAOS


Dive into the research topics of 'DAOS as HPC Storage, a view from Numerical Weather Prediction'. Together they form a unique fingerprint.

Cite this